Abstract: Abstract: Leaf chlorophyll content provides valuable information about the productivity, physiological status of vegetation. Measurement of hyperspectral reflectance offers a rapid, nondestructive method for leaf chlorophyll content estimation. In order to improve the accuracy of hyperspectral estimation about the leaf chlorophyll content, in this paper, the modeling of chlorophyll content of maize leaves based on the hyperspectrum was developed. The field experiments were conducted in the testing farm of Northwest Agriculture and Forest University, Yangling City, Shaanxi Province. During the maize growth period of milk stage, hyperspectral reflectance measurements were collected in wavelength of 350 to 2500 nm using spectrometer (SVC HR-1024i), and at the same time, chlorophyll content of maize leaves was obtained by using SPAD-502. There were totally 120 samples collected, two thirds of which were utilized as the training set and remaining one third as the validation set. The model constructed relied on the training set and the validation set was evaluated, respectively. The correlation between first derivative spectra, hyperspectral characteristic parameters and SPAD values were analyzed. Then single variable linear and nonlinear fitting traditional regression models respectively based on first derivative spectra and hyperspectral characteristic parameters were established to estimate the SPAD values. Besides, taken the first derivative values at 763 nm, the maximum first derivative values within blue edge (Db), red edge position (λr) and blue edge area (SDb) as the input parameters, the measured SPAD values as the output parameters, BP neural network model was built. By using the same input parameters, principal component regression (PCR) and partial least squares regression (PLSR) were used to estimate the SPAD values, too. Then we compared the predictive power of traditional regression models, PCR and PLSR models to BP neural network model. Some critical conclusions were made based on the study. First, the maximum correlation coefficient between SPAD values and first derivative spectra located at 763 nm (R=0.901) and the polynomial model was better than the linear model. As to the hyperspectral characteristic parameters, the variable among which the maximum first derivative values within blue edge (Db) was significantly related with SPAD values(R=-0.850) and its linear model was the best model of SPAD estimation models established by the hyperspectral characteristic parameters. The coefficients of determination for the calibration set of the two traditional regression models were 0.868 and 0.711, and the corresponding values of root mean square error (RMSE) were 3.069 and 4.340; for the validation set, the coefficients of determination were 0.864 and 0.743, and the values of RMSE were 3.186 and 4.317. Second, when using the BP neural network, PCR and PLSR established estimation models, the coefficients of determination for the calibration set were 0.887, 0.813 and 0.673 respectively, and the corresponding values of RMSE were 3.169, 3.495 and 5.797, respectively; for the validation set, the coefficients of determination were 0.896, 0.854 and 0.704, and the corresponding values of RMSE were 2.782, 3.221 and 6.034. Third, compared the five SPAD estimation models, BP neural network model achieved the best result in this research and the coefficients of determination of the calibration set and the validation set were highest, the value of RMSE of the validation set was lowest. The traditional regression model based on first derivative values at 763 nm performed second to BP neural network. Finally, BP neural network model had better predictive power of the chlorophyll content. The results showed that the method was a real-time and efficient method for maize leaf SPAD estimation. Our research may provide a theoretical basis for the improvement of remote sensing inversion accuracy of maize chlorophyll content.